6 papers
I-CAM-UV: Integrating Causal Graphs over Non-Identical Variable Sets Using Causal Additive Models with Unobserved Variables
Hirofumi Suzuki, Kentaro Kanamori, Takuya Takagi +3
Causal discovery from observational data is a fundamental tool in various fields of science. While existing approaches are typically designed for a single dataset, we often need to…
Sparse Additive Model Pruning for Order-Based Causal Structure Learning
Kentaro Kanamori, Hirofumi Suzuki, Takuya Takagi
Causal structure learning, also known as causal discovery, aims to estimate causal relationships between variables as a form of a causal directed acyclic graph (DAG) from observati…
Learning Decision Trees and Forests with Algorithmic Recourse
Kentaro Kanamori, Takuya Takagi, Ken Kobayashi +1
This paper proposes a new algorithm for learning accurate tree-based models while ensuring the existence of recourse actions. Algorithmic Recourse (AR) aims to provide a recourse a…
Computing the Collection of Good Models for Rule Lists
Kota Mata, Kentaro Kanamori, Hiroki Arimura
Since the seminal paper by Breiman in 2001, who pointed out a potential harm of prediction multiplicities from the view of explainable AI, global analysis of a collection of all go…
BRPO: Batch Residual Policy Optimization
Sungryull Sohn, Yinlam Chow, Jayden Ooi +4
In batch reinforcement learning (RL), one often constrains a learned policy to be close to the behavior (data-generating) policy, e.g., by constraining the learned action distribut…
Enumeration of Distinct Support Vectors for Interactive Decision Making
Kentaro Kanamori, Satoshi Hara, Masakazu Ishihata +1
In conventional prediction tasks, a machine learning algorithm outputs a single best model that globally optimizes its objective function, which typically is accuracy. Therefore, u…